The network architecture for the components used in our proposed method basically follows the ones in [1]}.
For all experiments, we adopt a reduced ResNet18 [2]} as our feature extractor \(\mathcal {F}\)  with resultant feature dimension set to 160, the projection head \(\mathcal {G}\)  is a two-layer multilayer perceptron (MLP) with width 160 and 128 respectively, and the predictor \(\mathcal {P}\)  is also a two-layer MLP with both input and output width being 128. The transformation module \(\mathcal {H}\)  of contrastive learning consists of random cropping, random horizontal flip, random colour distortion, and random grey scale. The detailed setting for transformation module \(\mathcal {H}\)  is described later in the next paragraph. We adopt the SGD optimiser with learning rate 0.1. The batch size \(\left|B^u_t\right|\)  is 10, and the retrieval batch size \(\left|B_M\right|\)  is 10.
We adopt the rotation as the SDA strategy \(\mathcal {S}\) , where \(\mathcal {S}\)  consists of four different degree of rotation (\(K=4\) ), i.e. \(0^{\circ }, 90^{\circ }, 180^{\circ },\)  and \(270^{\circ }\) . We empirically set \(\lambda \)  in the total loss function (cf. Eq. REF ) to \(1.5\) , and the temperature \(\tau _w\)  in our WABS (cf. Eq. REF ) to 0.5.
We use reservoir sampling [3]} and uniform random sampling for operations MemoryUpdate and MemoryRetrieval as described in Algorithm REF .
